Human Posture Detection Method Based on Wearable Devices

Posture monitoring in healthcare: a systematic mapping study and taxonomy

Palliative treatments for back pain usually include exercise, analgesics, physiotherapy, prostheses, and surgery in severe cases. Technologies for postural monitoring are growing, and they are important in preventing back pain and mitigating permanent damage. Remote work, especially after the COVID-19 pandemic, made people spend more time than usual in chairs and environments not certified by the health aspects of work. This research investigated through a Systematic Mapping Study (SMS) contributions in posture monitoring for healthcare in smart environments, including the different methods to obtain the posture, the limitations, and the target audience of the proposed models. The SMS was conducted in eight databases, including articles from January 2012 to March 2022. The initial search yielded 3161 articles, of which 34 were selected after applying the filtering criteria. Moreover, this study presents the challenges related to posture behavior monitoring, identifying studies and implementations that apply assistive technology for postural monitoring and improving the health and life of remote workers. In addition, three commercial postural devices are presented, and what challenges they currently face. Regarding healthcare, results showed a prevalence of using the Internet of Things (IoT) devices such as wireless sensor networks and inertial measurement unit (IMU) sensors. This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis.

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user’s physical sign parameters are abnormal or the user’s current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.

Machine Learning for Healthcare Wearable Devices: The Big Picture

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.